Bibliographic record
Abstract
This study examines the smuggling of Indian citizens to other countries and aims at gaining an insight into the modus operandi of the smugglers. It is based on an empirical study of deportation cases of Indian citizens from other countries. About three-quarters of the deportees who accessed the services of smugglers belonged to the State of Punjab. Almost 80 per cent of the deportees were young and in the age group of 18-30. About 28 per cent of the deportees succeeded in reaching their countries of destination with the help of smugglers. Thus, the success rate of illegal migration with the help of smugglers is higher by more than quarter than without them. Individual deportees had paid sums varying from US$ 2000 to US$ 10,000 to smugglers. No correlations have been observed between the countries of destination and the amounts paid to smugglers. The study has shown that smuggling of aliens is carried out by professional organizations with links in the countries of source, transition, and destination. The study concludes that so long as opportunities for legal migration are limited there will always be a demand for smugglers. In order to prevent and control smuggling an international co-operation among countries of source, transition, and destination would be imperative. It would also be desirable to widen the ambit of legal migration for low-skill jobs like in agriculture, construction works, and service sectors for limited periods through country-specific bilateral agreements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".